Project Summary Over the last decade, scientists have accelerated their efforts to understand Alzheimer?s disease (AD). This has led to unprecedented knowledge of the genetic and biological bases of AD risk, and vast stores of valuable data for further mining. Understanding the genetic and biological risk states for AD is, in itself, extraordinarily valuable for guiding mechanistic studies, developing better diagnostics, and formulating therapeutics. But an understanding of risk states also has the benefit of allowing research on resilience to AD. Research on the genetic and biological bases of resilience necessarily lags behind the discovery of risk factors. Now, as the risk architecture of AD is coming into view, it is feasible to study resilience to AD in individuals who are cognitively normal despite being at elevated risk for the disease. The approach we have devised for identifying resilience factors is straightforward yet, to our knowledge, unprecedented. We identify unaffected individuals at the highest levels of multivariate risk, match them to affected individuals at equivalent levels of risk, and contrast these two subgroups to find residual variation associated with the absence of disease. In this project, we will capitalize on the wealth of existing high-throughput AD risk-factor results and data, and our involvement in many of the world?s largest AD consortia, to efficiently map resilience to AD at three levels (genetics, transcriptomics, and neuroimaging), and to integrate across these levels. In Aim 1, we will identify genetic variation associated with resilience to AD in the presence of elevated genetic risk conferred by APOE ?4 alleles, an elevated AD polygenic risk score, or an elevated AD polygenic hazard score. In Aim 2, we will mega-analyze all available transcriptomic data from studies of postmortem hippocampal tissue and of peripheral blood in AD to identify transcriptomic risk scores and machine-learning algorithms that maximally distinguish AD from cognitively normal control subjects, and scores and algorithms that then identify residual transcriptomic variation that offsets the transcriptomic risk in resilient controls. In Aim 3, we will identify an MRI-based structural brain signature that is associated with resilience to AD in the presence of an AD- associated cortical risk signature. Lastly, in our exploratory Aim 4, we will integrate genetic, transcriptomic, brain structural, and clinical data to identify biological relationships across Aims, and novel phenotypes of resilience. Collectively, these Aims will identify multivariate, genetic, transcriptomic, and brain-structural profiles of resilience to AD, as well as molecular, neurobiological, and clinical phenotypes stemming from AD- resilience genotypes.